An Efficient Approach for Electricity Theft Detection Based on Transfer Learning in Temporal Sequence

Results in Engineering, Год журнала: 2025, Номер 26, С. 105125 - 105125

Опубликована: Май 2, 2025

Язык: Английский

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Deep ensembled voting framework for human activity recognition and validation on video sequences

Evolving Systems, Год журнала: 2025, Номер 16(2)

Опубликована: Май 30, 2025

Язык: Английский

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An Explainable AI System for Medical Image Segmentation With Preserved Local Resolution: Mammogram Tumor Segmentation

IEEE Access, Год журнала: 2023, Номер 11, С. 125543 - 125561

Опубликована: Янв. 1, 2023

Medical image segmentation aims to identify important or suspicious regions within medical images. However, many challenges are usually faced while developing networks for this type of analysis. First, preserving the original resolution is utmost importance task where identifying subtle features abnormalities can significantly impact accuracy diagnosis. The introduction dilated convolution module helped preserve in deep convolutional neural networks, but it has a drawback: loss local spatial due increased kernel sparsity checkboard patterns. To address this, work, double-dilated proposed maintain achieving large receptive field. This approach applied tumor breast cancer mammograms as proof-of-concept. Additionally, study tackles issue pixel-level class imbalance mammogram screenings by comparing various functions find best one mass segmentation. Our work also addresses "black-box" nature models performing quantitative and qualitative evaluations their interpretability using Gradient weighted Class Activation Map (Grad-CAM) with other explainable An experimental analysis on lesion performed from INBreast dataset, both before after integrating dilation into state-of-the-art network. results demonstrate effectiveness terms Dice similarity Miss Detection rate promotes Tversky Loss function training pixel-imbalanced data Grad-CAM explaining results.

Язык: Английский

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Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

Journal of Cardiovascular Development and Disease, Год журнала: 2023, Номер 10(12), С. 485 - 485

Опубликована: Дек. 4, 2023

Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) a high-resolution imaging solution that can image coronary arteries, but software via wall segmentation and quantification been evolving. In this study, deep learning (DL) paradigm was explored along with bias.Using PRISMA model, 145 best UNet-based non-UNet-based methods for were selected analyzed their characteristics scientific clinical validation. This study computed thickness by estimating inner outer borders of IVUS cross-sectional scans. Further, review bias in DL system first time when it comes to Three methods, namely (i) ranking, (ii) radial, (iii) regional area, applied compared using Venn diagram. Finally, presented explainable AI (XAI) paradigms framework.UNet provides powerful walls scans due ability extract automated features at different scales encoders, reconstruct segmented decoders, embed variants skip connections. Most research hampered lack motivation XAI pruned (PAI) models. None UNet models met criteria bias-free design. For assessment settings, necessary move from paper-to-practice approach.

Язык: Английский

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Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Applied Sciences, Год журнала: 2023, Номер 13(18), С. 10270 - 10270

Опубликована: Сен. 13, 2023

This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training with new images? (3) learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four models—AlexNet, ResNet-50, MobileNet, VGG-19—were five rounds incrementally adding images baseline set comprising 11,538 chest X-ray images. In each round, models were tested decreasing levels of image similarity. Notably, all showed performance drops when containing outlier or sourced other clinics. Round 1, 95.2~99.2% was for Level 1 testing (i.e., same clinic but apart only), 94.7~98.3% 2 an external similar). However, drastically decreased 3 rotation deformation), mean sensitivity plummeting 99% 36%. For 4 another clinic), 97% 86%, 67%. Rounds 3, 25% 50% improved average Level-3 15% 23% 56% 71% 83%). 5, increased Level-4 81% 92% 95%, respectively. Among models, ResNet-50 demonstrated most robust across five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps intermediate activation visual correlations pneumonia manifestations insufficient explicitly explain classification. heatmaps at shed light progression models’ learning behavior.

Язык: Английский

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Emerging Technology-Driven Hybrid Models for Preventing and Monitoring Infectious Diseases: A Comprehensive Review and Conceptual Framework

Diagnostics, Год журнала: 2023, Номер 13(19), С. 3047 - 3047

Опубликована: Сен. 25, 2023

The emergence of the infectious diseases, such as novel coronavirus, a significant global health threat has emphasized urgent need for effective treatments and vaccines. As diseases become more common around world, it is important to have strategies in place prevent monitor them. This study reviews hybrid models that incorporate emerging technologies preventing monitoring diseases. It also presents comprehensive review employed since outbreak COVID-19. encompasses integrate innovative technologies, blockchain, Internet Things (IoT), big data, artificial intelligence (AI). By harnessing these system enables secure contact tracing source isolation. Based on review, conceptual framework model proposes incorporates technologies. proposed tracing, isolation using blockchain technology, IoT sensors, data collection. A approach With continued research development model, efforts effectively combat safeguard public will continue.

Язык: Английский

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Resnet Transfer Learning For Enhanced Medical Image Classification In Healthcare

Опубликована: Дек. 29, 2023

This work overcomes the limitations of sparsely labeled data by optimizing ResNet transfer learning methods in medical classification images. Using a deductive approach along with interpretive philosophy, we optimize for better diagnostic performance on healthcare sets. Our team technical includes preprocessing datasets, configuring model architectures, and fine-tuning hyperparameters using secondary data. The improved as demonstrated results is confirmed metrics such precision, reliability, recall. Analyses comparisons demonstrate superiority over basic models. Upcoming tasks include working together to create standardized benchmarks, improving interpretability scalability, verifying actual clinical settings.

Язык: Английский

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Exploiting Machine Learning and LSTM for Human Activity Recognition: Using Physiological and Biological Sensor Data from Actigraph

2022 IEEE International Conference on Industrial Technology (ICIT), Год журнала: 2024, Номер 3, С. 1 - 8

Опубликована: Март 25, 2024

Human activity recognition involves identifying the daily living activities of an individual through utilization sensor attributes and intelligent learning algorithms. The identification intricate human proves to be a labo-rious task, given inherent difficulty capturing long-term dependencies extracting efficient features from unprocessed data. For this purpose, study aims at recognizing classifying using physiological biological data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected body composition also better suited for self-monitoring. We examined effectiveness these applying prevalent machine classifiers long short-term memory (LSTM) networks on recently publicly available data, includes accelerometer heart rate recordings. results our experiments showed that LSTM models performed than conventional ML with best result achieving accuracy 86.5%. findings confirms significance in more.

Язык: Английский

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Augmenting Radiological Diagnostics with AI for Tuberculosis and COVID-19 Disease Detection: Deep Learning Detection of Chest Radiographs

Diagnostics, Год журнала: 2024, Номер 14(13), С. 1334 - 1334

Опубликована: Июнь 24, 2024

In this research, we introduce a network that can identify pneumonia, COVID-19, and tuberculosis using X-ray images of patients' chests. The study emphasizes tuberculosis, healthy lung conditions, discussing how advanced neural networks, like VGG16 ResNet50, improve the detection issues from images. To prepare for model's input requirements, enhanced them through data augmentation techniques training purposes. We evaluated performance by analyzing precision, recall, F1 scores across training, validation, testing datasets. results show ResNet50 model outperformed with accuracy resilience. It displayed superior ROC AUC values in both validation test scenarios. Particularly impressive were ResNet50's precision recall rates, nearing 0.99 all conditions set. On hand, also performed well during testing-detecting 0.93. Our highlights our deep learning method showcasing effectiveness over traditional approaches VGG16. This progress utilizes methods to enhance classification augmenting balancing them. positions approach as an advancement state-of-the-art applications imaging. By enhancing reliability diagnosing ailments such COVID-19 models have potential transform care treatment strategies, highlighting their role clinical diagnostics.

Язык: Английский

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MultiNet 2.0: A Lightweight Attention-based Deep Learning Network for Stenosis measurement in Carotid Ultrasound scans and Cardiovascular Risk Assessment

Computerized Medical Imaging and Graphics, Год журнала: 2024, Номер 117, С. 102437 - 102437

Опубликована: Окт. 1, 2024

Язык: Английский

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